Adaptive and Mobility-predictive Quantization-based Communication Data Management for High Performance Distributed Computing

Communication data management (CDM) is an important issue in high performance distributed computing where a massive amount of data exchange frequently occurs among geographically distributed components. In this paper, we review existing CDM schemes in distributed computing systems and we propose more efficient CDM schemes. Three types of quantization-based CDM schemes are proposed: the fixed quantization-based CDM (FQ-CDM), the adaptive quantization-based CDM (AQ-CDM), and the mobility-predictive quantization-based CDM (MPQ-CDM). The FQ-CDM applies a basic theory of quantized systems to the distributed computing environment. The AQ-CDM uses a communication object clustering mechanism, which operates a pattern recognition clustering algorithm. The MPQ-CDM predicts the next states of communication objects by using past and current data and controls data communication among communication objects. The mobile object location monitoring system (MOLMS), based on High Level Architecture, is designed and developed to apply these CDM schemes to distributed computing. In this paper we conduct experiments by comparing these CDM schemes with each other on the MOLMS. The experimental results show that the AQCDM is the more effective scheme for communication message reduction and the MPQ-CDM is the more suitable scheme for mobile location error reduction.